Version 1
: Received: 18 September 2023 / Approved: 19 September 2023 / Online: 20 September 2023 (08:59:34 CEST)
How to cite:
Ahmad, I. Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors. Preprints2023, 2023091318. https://doi.org/10.20944/preprints202309.1318.v1
Ahmad, I. Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors. Preprints 2023, 2023091318. https://doi.org/10.20944/preprints202309.1318.v1
Ahmad, I. Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors. Preprints2023, 2023091318. https://doi.org/10.20944/preprints202309.1318.v1
APA Style
Ahmad, I. (2023). Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors. Preprints. https://doi.org/10.20944/preprints202309.1318.v1
Chicago/Turabian Style
Ahmad, I. 2023 "Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors" Preprints. https://doi.org/10.20944/preprints202309.1318.v1
Abstract
Machine Learning (ML) has brought about a transformative era in reactor operations, reshaping monitoring, control, and optimization strategies. This survey comprehensively explores the diverse spectrum of ML applications in industrial reactors. From real-time sensor analysis ensuring reactor functionality to adaptive control algorithms ensuring stability, ML's impact is profound and multifaceted. The benefits of ML are equally evident in optimization, encompassing performance trend prediction, proactive maintenance, and fine-tuning of operations for enhanced efficiency. This review identifies dominant ML techniques, operations stages receptive to ML integration, core data sources, and critical input-output parameters. Aimed at both academics and practitioners, this exploration enriches reactor technologies, unlocking their full potential through insights driven by ML.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
